FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models
FIRE is a training-free method for multi-fidelity regression that combines tabular foundation models with distribution-conditioned in-context learning. It uses a low-fidelity model to predict distributions, then conditions a high-fidelity correction model on these distributions to estimate the true target function. This allows for accurate predictions even when high-fidelity data is extremely scarce.